ECE 477 Patent Liability Team 5 – Fall 2012

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ECE 477 Patent Liability Team 5 – Fall 2012

description

ECE 477 Patent Liability Team 5 – Fall 2012. Sensor Type/ Placement. Data Processing. Monitoring Device. Data Transmission. Online Database. Laptop connection. 3. 2. 1. Project Overview. Base-station. Riddell - HITS System. X2Impact – X-band. Potential Prototype Rendering. - PowerPoint PPT Presentation

Transcript of ECE 477 Patent Liability Team 5 – Fall 2012

Page 1: ECE 477  Patent Liability Team 5 – Fall 2012

ECE 477 Patent Liability

Team 5 – Fall 2012

Page 2: ECE 477  Patent Liability Team 5 – Fall 2012

Project Overview

Online Database

Laptop connection

Base-station

Monitoring Device

1

23

Sensor Type/ Placement

Data Transmission

Data Processing

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Current DevicesX2Impact – X-bandRiddell - HITS System

Potential Prototype Rendering

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Riddell - US Patent #1» Patent #: 6,826,509 B2» Filed: 10-10-2001» Date of Patent: 11-30-2004» Title: System and Method for Measuring the Linear and Rotational Acceleration of a

Body Part

» Sensor Type/ Placement Claims: ˃ 2&3 state single or multi-axis accelerometers may be used˃ 12, 13 & 14 refer directly to head placement being either in a helmet or headband˃ 18 claims the sensors may be mounted on any “geometric shape” on the body

» Data Transmissions Claims: ˃ 10 covers wired transmission˃ 11 covers radio transmission

» Data Processing Claims: ˃ 17 A hit profile is created using normalized vectors to show a hit occurred˃ 42 Employs a least squares regression model to determine best fit profile

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X2Impact- Pending Patent #1

» Patent Application #: 20110181419» Filed: 07-28-2011» Title: Head Impact Event Reporting System

» Claim 1: A computer implemented method for event detection comprising: collecting sensor data detected by a sensor attached to a head of a user, the sensor monitoring an impact parameter associated with an impact event; determining a time since the impact event; calculating an assessment score as a function of the impact parameter and the time since the impact event. 

» Main Differentiating Factor: Data Processing˃ Claim 10: a total assessment score is a sum of the

assessment score for each one of the plurality of impact events. 

˃ Claim 11: …wherein the assessment score is multiplied by a weight related to the time since the impact event…

˃ Claim 12: …the assessment score equals the weight times an impact score…

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X2Impact- Pending Patent #2

» Patent Application #: 20110184663» Filed: 10-10-2001» Title: Head Impact Analysis and Comparison System» Claim 1: A computer implemented method for head impact analysis comprising:

receiving impact data related to an impact parameter for an impact event experienced by a head of a user; comparing the impact data to previously stored impact parameter data in a data store, wherein the previously stored impact parameter data is associated with prior impact events; and generating an association between the impact event and one or more of the prior impact events. 

» Claim 16: The computer implemented method of claim 15, wherein the stored programming instructions further cause the processor to determine a percentage of associated events in which the prior impact events experienced a concussion.

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Analysis of Patent Liability» Literally infringing functions

˃ Accelerometers mounted on head˃ Accelerometers measure acceleration on the head˃ Sensor data transmitted wirelessly to “base-station”˃ Sensor data is processed to determine impact force and

location

» Answer: » Obtain license/ pay royalty fee

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Analysis of Patent Liability» Infringement under doctrine of equivalents

˃ Data from head mounted sensors is processed to predict potential brain injury

» Answer: » Use gyroscopes to collect the rotational data» Patent a better way to process the stored data

and then co-license or sell rights (What use is the device if it can’t predict well?)

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Answer – New Prediction Method

Is not binary -> Current: Concussed or not concussed (stupid thresholds)

Proposed Method- Create an adaptive network˃ Has an initial training set of size n created from PNG data <Player, Impact # in last month, Time from last impact, Location, Peak Force, Decay Time, Angle of Rotation, % Change in MRI morphology, ABET rating, IMPACT test score>

˃ Once system is trained it will be able to make predictions but may still be trained with available values to refine output

˃ System will output predicted regional change in brain tissue morphology -> This allows better prediction to whether or not critical damage has occurred and how to prevent further damage to regions showing susceptibility

Non-Concussed Concussed mTBI

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Questions?